1. A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
- Author
-
Bowei Feng, Huisheng Zhang, Tianfu Wang, Qiaoliang Li, Linpei Xie, and Ping Liang
- Subjects
Databases, Factual ,Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Image processing ,02 engineering and technology ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Image texture ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Segmentation ,Computer vision ,Electrical and Electronic Engineering ,Feature detection (computer vision) ,Radiological and Ultrasound Technology ,Pixel ,business.industry ,Segmentation-based object categorization ,Retinal Vessels ,Pattern recognition ,Image segmentation ,Computer Science Applications ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.
- Published
- 2016